Usually link adaptation technology uses the received Signal-to-Noise Ratio (SNR) as the channel quality information (CQI) in OFDM systems. Therefore, adaptive modulation and coding can be performed on the basis of different SNR ranges. Within each adaptation time window T for adaptive modulation and coding (AMC) scheme, the received SNR may be located in any one of the pre-defined SNR grids (as shown in FIG. 1). γ represents the average value of SNR at the receiver and M represents the related modulation and coding scheme. For example, if the received SNR value γ is within a range of γ1 to γ2, then modulation and coding scheme M1 will be adopted. This method is also referred to as “threshold method”. In addition, there are also other methods using bit error ratio (BER)/package error ratio (PER) for CQI in link adaptation. In this way, the receiver will calculate the BER/PER, instead of SNR, of each frame/block transmission. The related process is exactly the same as above.
Conventional threshold method can work well under slow time-varying channels like wireless indoor environment. Since the channel fading tends to be flat and slow varying, a fixed adaptation window will satisfy the requirements for tracking the slow-varying channel. For wireless local area network (WLAN) systems, the wireless indoor environments are often flat, slow-varying channels in an update period T, which is easy to process and equalize.
But in fast time-varying environments, e.g. where the receiver is moving at a high speed outside, the conventional method may not be able to track the fast-fading channel with the pre-defined update period T. That is, the update period T is relatively large and the current CQI may be invalid for the next transmission with practical feedback delay. In this case, channel prediction or other methods should be used to solve fast time-varying channel and large feedback delay.
Another problem exists even if the update period T is appropriate. The conventional method usually uses the average SNR of a received signal within time T to determine a modulation and coding scheme (MCS) that is supposed to match the varying channel well. However, the average SNR may not describe the features of the fading channel well enough. Via multi-path channel, it is popular for the received signal to experience deep channel fading, or “slopes”, within the time window T. These “slopes”, compared with flat-fading envelopes, are more possible to incur bit errors with the determined MCS. In the time length on which “slopes” occurred, the determined MCS may not perform as well as expected and system outage will probably occur. Simulation results have proved that MCS selection based on average SNR cannot give the optimal link adaptation.
As shown in FIG. 2, Rayleigh fading is often used to represent a received signal envelope in wireless systems. This envelope tends to contain a plurality of discontinuous “slopes” within a large-scaled time window T. However, if we observe the received SNR, we will find that the development trace of the SNR envelope tracks the amplitude of the signal, but tends to be easily formed within an identical time window (shown in FIG. 3).
Such a case also applies to sub-carriers of single-carrier systems and multi-carrier systems. However, if we choose to observe SNR curves over a time no larger than channel coherence time within the time window T, the SNR envelope then tends to be flat (shown in FIG. 4).
Assuming that the channel quality indicator (here, we use SNR) is measured over an arbitrary time window (no larger than channel coherence time) set by the link adaptation protocol. In multi-carrier modulation system, a two-dimensional time-frequency window may be used. Theoretically, the mapping from SNR information to MCS should be determined by the probability density function (pdf) of the SNR over that period. However, in real channels the pdf cannot be obtained by simple analysis since it is in fact a function of quite a few parameters. Typically, for single-carrier systems, pdf depends on the channel fading statistics over the adaptation window, but it also depends on the relationship between the length of the window in time and the channel coherence time. For wireless OFDM systems, it also depends on the relationship between the length of the adaptation window in frequency and the channel coherence frequency bandwidth.
In order to estimate the pdf accurately, we simplify the problem by estimating limited statistical information from the pdf, such as the k-order moment over the adaptation window. The k-order moments are useful when k is equal to 2 and yet yield sufficient information for a reasonably accurate mapping of the SNR into MCS information. Here, we recommend using the second order moment of the SNR (the SNR variance). The average SNR used in the conventional method is only the pure mean (first order moment) and gives how much power is measured at the receiver on average. The second order moment (variance) of the SNR over the time/frequency dimension captures the useful information on the time/frequency selectivity of the channel within the adaptation window. The larger the variance, the stronger the fading selectivity on time/frequency dimension is. Therefore, if the current variance is relatively large, it is beneficial to shrink the adaptation window such that the channel fading pdf tends to be flat and results in a smaller variance. The SNR variance on frequency can be used to measure the suitable size of subgroup in adaptive OFDM system. The modification on the adaptation period T can improve the effectiveness and accuracy of the mapping from SNR to MCS in the given time window, but may need the cooperation of Link Adaptation (LA) protocol to signal the update of adaptation period T.
The conventional method uses the SNR grids as shown in FIG. 1 to determine which MCS to be adopted for next transmission. In the solution proposed here, the average value of SNR and the variance of SNR will contribute to the MCS selection in link adaptation.